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Record W3006417743 · doi:10.1162/glep_r_00552

<i>Africa’s Gene Revolution: Genetically Modified Crops and the Future of African Agriculture</i>. By Matthew A. Schnurr. 2019. Montreal, QC: McGill-Queen’s Press.

2020· article· en· W3006417743 on OpenAlexaboutno aff
Robert Paarlberg

Bibliographic record

VenueGlobal Environmental Politics · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureCropFood securityAgricultural biotechnologyAgricultural economicsPolitical scienceEconomicsGeographyBiologyAgronomy

Abstract

fetched live from OpenAlex

Matthew Schnurr’s book, Africa’s Gene Revolution, stakes out an important position. It argues that genetically modified crops (commonly known as GMOs) have not been widely taken up in Africa because they fail to meet the needs of the numerous smallholder farmers on that continent. GMO crops can deliver benefits on some large-scale commercial farms (operated by white farmers in South Africa), and on some large irrigated farms (cotton farms in Sudan), but for poor smallholder farmers, the seeds are less valuable. For example, GMO cotton seeds protect against the wrong crop pests in Uganda, and they have produced low-quality fibers in Burkina Faso. They are developed and disseminated top-down, by distant researchers who do not understand the social and economic circumstances of poor farmers.Schnurr builds his case from an impressive research effort inside Africa, including more than 125 interviews conducted over a decade with local informants, as well as ranking exercises with more than 200 farmers. The result is a detailed country-by-country, crop-by-crop account, packed with valuable technical and institutional detail. The language is clear and the tone is admirably nonpolemical. The frequently alleged food safety and environmental safety risks associated with GMOs are for the most part not evaluated in the book, in part because the author has concluded that they are “relatively minimal.” Instead, the focus is on whether GMO seeds are “pro-poor,” and the author’s judgment is negative, because the seeds seldom fit the distinct needs and circumstances of smallholder farmers.Missing from Schnurr’s work is an alternative explanation for why GMO crops have failed to spread in Africa, an explanation based on the regulatory actions of African governments. In nearly all of Africa, government regulations have made it illegal for farmers to plant GMO crops. Schnurr may be right about the new seeds being a poor fit to the needs of smallholders, but how can we know, so long as government “biosafety” policies have blocked farmers from even trying the seeds? The only country in sub-Saharan Africa to have fully legalized the planting of any GMO food crops so far has been the Republic of South Africa, and there they have been widely successful.Schnurr ignores this regulatory blockage. In his chapter on rules and regulations, he describes Uganda, Ghana, Nigeria, Malawi, Ethiopia, Mozambique, Swaziland, and Cameroon as “emerging adopters” of GMO crops, yet among these countries, only the governments of Nigeria and Ethiopia have so far approved the commercial planting of any GMO crops at all, and the only crop approved has been cotton, not a food crop. This lack of approval means that food crop producers in all of these countries, along with the rest of the region, are still legally blocked from planting GMO varieties of maize, rice, yams, cassava, or banana. Some African countries do have significant GMO crop research programs (Kenya has been doing research on GMOs since the 1990s, as Schnurr points out), but they have yet to give their farmers permission to grow these crops.Schnurr’s analysis also downplays the source of Africa’s highly precautionary GMO regulations. They were brought in primarily from Europe, through NGOs, foreign assistance agencies, and the United Nations system. Highly precautionary biosafety regulations have kept GMOs out of farm fields in Europe, so it is no surprise that similar regulations copied from Europe would do the same in Africa. Schnurr correctly identifies the African Model Law of the Organization of African Unity (OAU) as an umbrella instrument framing these policies, but he fails to mention that promoting this model law in Africa was a German foreign assistance project. Schnurr discusses Zambia’s rejection of GMO food aid, but little or no attention is given to the role of European NGOs in scaring the Zambians away from the aid. One organization from the United Kingdom, named Farming and Livestock Concern, even told the Zambians that GMO maize might introduce a retrovirus similar to HIV (Paarlberg 2008).While underplaying Europe’s blocking efforts in Africa, Schnurr also exaggerates the promotional efforts made by GMO supporters, such as USAID and the Bill and Melinda Gates Foundation. USAID’s Program for Biosafety Systems (PBS) actually reinforced the risk narratives around GMOs by training African regulators to do separate risk assessments for these seeds. When the Bill and Melinda Gates Foundation launched its Alliance for a Green Revolution in Africa (AGRA), the first board chair, Kofi Anan, specifically excluded GMO crops from the technologies AGRA would develop and promote (Southgate 2007).Schnurr’s book can still be used to good advantage, especially by those with research interests in the case studies. Schnurr’s work on the Insect Resistant Maize and Water Efficient Maize projects, and on cotton in Sudan, Burkina Faso, and South Africa, is well informed and up to date. Yet even here something is missing. When Schnurr concludes that GMO cotton will not work for the kind of smallholder farmers found in Africa, he never mentions that smallholders in China and India have been using Bt cotton widely and successfully for several decades now. Most Bt cotton farmers in India are very small, planting only three to four acres of the crop. Bt cotton spread rapidly in these two countries as soon as government regulators legalized use of the seeds; African farmers in cotton-growing countries like Uganda, Kenya, and Tanzania deserve a similar chance to try the seeds in their own fields.Norman Borlaug, the Nobel Prize–winning scientist who grew up on a small farm in Iowa, and who launched the original green revolution in South Asia in the 1960s, uttered these last words before he passed away: “Take it to the farmer” (Quinn 2010). That would be good advice for Africa today.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.167
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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